----------------------
For each samples in ``source``, find its closest neighour in ``centers``.
-.. ocv:function:: void ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat())
-
- :param dists: The output distances calculated from each sample to the best matched center.
-
- :param labels: The output index of best matched center for each row of sample.
+.. ocv:function:: void ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR)
:param src: Floating-point matrix of input samples. One row per sample.
:param distType: Distance metric to calculate distances. Supports ``NORM_L1`` and ``NORM_L2SQR``.
- :param indices: Optional source indices. If not empty:
+ :param dists: The output distances calculated from each sample to the best matched center.
- * only the indexed source samples will be processed
- * outputs, i.e., ``dists`` and ``labels``, have the same size of indices
- * outputs are in the same order of indices instead of the order of src
+ :param labels: The output index of best matched center for each row of sample.
The method is a utility function which maybe used for multiple clustering algorithms such as K-means.
// supports NORM_L1 and NORM_L2 distType
// if indices is provided, only the indexed rows will be calculated and their results are in the same
// order of indices
- CV_EXPORTS void distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType = NORM_L2SQR, const oclMat &indices = oclMat());
+ CV_EXPORTS void distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType = NORM_L2SQR);
//!Does k-means procedure on GPU
// supports CV_32FC1/CV_32FC2/CV_32FC4 data type
//////////////////////////////distanceToCenters////////////////////////////////////////////////
-CV_ENUM(DistType, NORM_L1, NORM_L2SQR);
+CV_ENUM(DistType, NORM_L1, NORM_L2SQR)
+
typedef tuple<Size, DistType> distanceToCentersParameters;
typedef TestBaseWithParam<distanceToCentersParameters> distanceToCentersFixture;
static void distanceToCentersPerfTest(Mat& src, Mat& centers, Mat& dists, Mat& labels, int distType)
{
Mat batch_dists;
- cv::batchDistance(src,centers,batch_dists, CV_32FC1, noArray(), distType);
+ cv::batchDistance(src, centers, batch_dists, CV_32FC1, noArray(), distType);
+
std::vector<float> dists_v;
std::vector<int> labels_v;
- for(int i = 0; i<batch_dists.rows; i++)
+
+ for (int i = 0; i < batch_dists.rows; i++)
{
Mat r = batch_dists.row(i);
double mVal;
Point mLoc;
+
minMaxLoc(r, &mVal, NULL, &mLoc, NULL);
- dists_v.push_back((float)mVal);
+ dists_v.push_back(static_cast<float>(mVal));
labels_v.push_back(mLoc.x);
}
- Mat temp_dists(dists_v);
- Mat temp_labels(labels_v);
- temp_dists.reshape(1,1).copyTo(dists);
- temp_labels.reshape(1,1).copyTo(labels);
+
+ Mat(dists_v).copyTo(dists);
+ Mat(labels_v).copyTo(labels);
}
PERF_TEST_P(distanceToCentersFixture, distanceToCenters, ::testing::Combine(::testing::Values(cv::Size(256,256), cv::Size(512,512)), DistType::all()) )
{
Size size = get<0>(GetParam());
int distType = get<1>(GetParam());
- Mat src(size, CV_32FC1);
- Mat centers(size, CV_32FC1);
- Mat dists(cv::Size(src.rows,1), CV_32FC1);
- Mat labels(cv::Size(src.rows,1), CV_32SC1);
+
+ Mat src(size, CV_32FC1), centers(size, CV_32FC1);
+ Mat dists(src.rows, 1, CV_32FC1), labels(src.rows, 1, CV_32SC1);
+
declare.in(src, centers, WARMUP_RNG).out(dists, labels);
+
if (RUN_OCL_IMPL)
{
- ocl::oclMat ocl_src(src);
- ocl::oclMat ocl_centers(centers);
- ocl::oclMat ocl_dists(dists);
- ocl::oclMat ocl_labels(labels);
+ ocl::oclMat ocl_src(src), ocl_centers(centers);
- OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src, ocl_centers, distType);
-
- ocl_dists.download(dists);
- ocl_labels.download(labels);
+ OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_src, ocl_centers, dists, labels, distType);
SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE);
SANITY_CHECK(labels);
}
else if (RUN_PLAIN_IMPL)
{
- TEST_CYCLE() distanceToCentersPerfTest(src,centers,dists,labels,distType);
+ TEST_CYCLE() distanceToCentersPerfTest(src, centers, dists, labels, distType);
+
SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE);
SANITY_CHECK(labels);
}
}
}
-void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType, const oclMat &indices)
+void cv::ocl::distanceToCenters(const oclMat &src, const oclMat ¢ers, Mat &dists, Mat &labels, int distType)
{
- CV_Assert(src.cols*src.oclchannels() == centers.cols*centers.oclchannels());
+ CV_Assert(src.cols * src.channels() == centers.cols * centers.channels());
CV_Assert(src.depth() == CV_32F && centers.depth() == CV_32F);
- bool is_label_row_major = false;
- ensureSizeIsEnough(1, src.rows, CV_32FC1, dists);
- if(labels.empty() || (!labels.empty() && labels.rows == src.rows && labels.cols == 1))
- {
- ensureSizeIsEnough(src.rows, 1, CV_32SC1, labels);
- is_label_row_major = true;
- }
CV_Assert(distType == NORM_L1 || distType == NORM_L2SQR);
- std::stringstream build_opt_ss;
- build_opt_ss
- << (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST")
- << (indices.empty() ? "" : " -D USE_INDEX");
-
- String build_opt = build_opt_ss.str();
+ dists.create(src.rows, 1, CV_32FC1);
+ labels.create(src.rows, 1, CV_32SC1);
- const int src_step = (int)(src.oclchannels() * src.step / src.elemSize());
- const int centers_step = (int)(centers.oclchannels() * centers.step / centers.elemSize());
-
- const int colsNumb = centers.cols*centers.oclchannels();
-
- const int label_step = is_label_row_major ? (int)(labels.step / labels.elemSize()) : 1;
- String kernelname = "distanceToCenters";
-
- const int number_of_input = indices.empty() ? src.rows : indices.size().area();
+ std::stringstream build_opt_ss;
+ build_opt_ss << (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST");
- const int src_offset = (int)src.offset/src.elemSize();
- const int centers_offset = (int)centers.offset/centers.elemSize();
+ int src_step = src.step / src.elemSize1();
+ int centers_step = centers.step / centers.elemSize1();
+ int feature_width = centers.cols * centers.oclchannels();
+ int src_offset = src.offset / src.elemSize1();
+ int centers_offset = centers.offset / centers.elemSize1();
- size_t globalThreads[3] = {number_of_input, 1, 1};
+ int all_dist_count = src.rows * centers.rows;
+ oclMat all_dist(1, all_dist_count, CV_32FC1);
vector<pair<size_t, const void *> > args;
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
args.push_back(make_pair(sizeof(cl_mem), (void *)¢ers.data));
- if(!indices.empty())
- {
- args.push_back(make_pair(sizeof(cl_mem), (void *)&indices.data));
- }
- args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
- args.push_back(make_pair(sizeof(cl_mem), (void *)&dists.data));
- args.push_back(make_pair(sizeof(cl_int), (void *)&colsNumb));
+ args.push_back(make_pair(sizeof(cl_mem), (void *)&all_dist.data));
+
+ args.push_back(make_pair(sizeof(cl_int), (void *)&feature_width));
args.push_back(make_pair(sizeof(cl_int), (void *)&src_step));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_step));
- args.push_back(make_pair(sizeof(cl_int), (void *)&label_step));
- args.push_back(make_pair(sizeof(cl_int), (void *)&number_of_input));
+ args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers.rows));
+
args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_offset));
+ size_t globalThreads[3] = { all_dist_count, 1, 1 };
+
openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
- kernelname, globalThreads, NULL, args, -1, -1, build_opt.c_str());
+ "distanceToCenters", globalThreads, NULL, args, -1, -1, build_opt_ss.str().c_str());
+
+ Mat all_dist_cpu;
+ all_dist.download(all_dist_cpu);
+
+ for (int i = 0; i < src.rows; ++i)
+ {
+ Point p;
+ double minVal;
+
+ Rect roi(i * centers.rows, 0, centers.rows, 1);
+ Mat hdr(all_dist_cpu, roi);
+
+ cv::minMaxLoc(hdr, &minVal, NULL, &p);
+
+ dists.at<float>(i, 0) = static_cast<float>(minVal);
+ labels.at<int>(i, 0) = p.x;
+ }
}
+
///////////////////////////////////k - means /////////////////////////////////////////////////////////
+
double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
TermCriteria criteria, int attempts, int flags, oclMat &_centers)
{
break;
// assign labels
- oclMat _dists(1, N, CV_64F);
-
- _bestLabels.upload(_labels);
+ Mat dists(1, N, CV_64F);
_centers.upload(centers);
+ distanceToCenters(_src, _centers, dists, _labels);
+ _bestLabels.upload(_labels);
- distanceToCenters(_dists, _bestLabels, _src, _centers);
-
- Mat dists;
- _dists.download(dists);
- _bestLabels.download(_labels);
float* dist = dists.ptr<float>(0);
compactness = 0;
for( i = 0; i < N; i++ )
- {
- compactness += (double)dist[i];
- }
+ compactness += (double)dist[i];
}
if( compactness < best_compactness )
- {
best_compactness = compactness;
- }
}
return best_compactness;
//
//M*/
+static float distance_(__global const float * center, __global const float * src, int feature_length)
+{
+ float res = 0;
+ float4 v0, v1, v2;
+ int i = 0;
+
#ifdef L1_DIST
-# define DISTANCE(A, B) fabs((A) - (B))
-#elif defined L2SQR_DIST
-# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
-#else
-# define DISTANCE(A, B) ((A) - (B)) * ((A) - (B))
+ float4 sum = (float4)(0.0f);
#endif
-inline float dist(__global const float * center, __global const float * src, int feature_cols)
-{
- float res = 0;
- float4 tmp4;
- int i;
- for(i = 0; i < feature_cols / 4; i += 4, center += 4, src += 4)
+ for ( ; i <= feature_length - 4; i += 4)
{
- tmp4 = vload4(0, center) - vload4(0, src);
+ v0 = vload4(0, center + i);
+ v1 = vload4(0, src + i);
+ v2 = v1 - v0;
#ifdef L1_DIST
- tmp4 = fabs(tmp4);
+ v0 = fabs(v2);
+ sum += v0;
#else
- tmp4 *= tmp4;
+ res += dot(v2, v2);
#endif
- res += tmp4.x + tmp4.y + tmp4.z + tmp4.w;
}
- for(; i < feature_cols; ++i, ++center, ++src)
+#ifdef L1_DIST
+ res = sum.x + sum.y + sum.z + sum.w;
+#endif
+
+ for ( ; i < feature_length; ++i)
{
- res += DISTANCE(*src, *center);
+ float t0 = src[i];
+ float t1 = center[i];
+#ifdef L1_DIST
+ res += fabs(t0 - t1);
+#else
+ float t2 = t0 - t1;
+ res += t2 * t2;
+#endif
}
+
return res;
}
-// to be distinguished with distanceToCenters in kmeans_kernel.cl
-__kernel void distanceToCenters(
- __global const float *src,
- __global const float *centers,
-#ifdef USE_INDEX
- __global const int *indices,
-#endif
- __global int *labels,
- __global float *dists,
- int feature_cols,
- int src_step,
- int centers_step,
- int label_step,
- int input_size,
- int K,
- int offset_src,
- int offset_centers
-)
+__kernel void distanceToCenters(__global const float * src, __global const float * centers,
+ __global float * dists, int feature_length,
+ int src_step, int centers_step,
+ int features_count, int centers_count,
+ int src_offset, int centers_offset)
{
int gid = get_global_id(0);
- float euDist, minval;
- int minCentroid;
- if(gid >= input_size)
- {
- return;
- }
- src += offset_src;
- centers += offset_centers;
-#ifdef USE_INDEX
- src += indices[gid] * src_step;
-#else
- src += gid * src_step;
-#endif
- minval = dist(centers, src, feature_cols);
- minCentroid = 0;
- for(int i = 1 ; i < K; i++)
+
+ if (gid < (features_count * centers_count))
{
- euDist = dist(centers + i * centers_step, src, feature_cols);
- if(euDist < minval)
- {
- minval = euDist;
- minCentroid = i;
- }
+ int feature_index = gid / centers_count;
+ int center_index = gid % centers_count;
+
+ int center_idx = mad24(center_index, centers_step, centers_offset);
+ int src_idx = mad24(feature_index, src_step, src_offset);
+
+ dists[gid] = distance_(centers + center_idx, src + src_idx, feature_length);
}
- labels[gid * label_step] = minCentroid;
- dists[gid] = minval;
}
int type;
int K;
int flags;
- cv::Mat src ;
+ Mat src ;
ocl::oclMat d_src, d_dists;
Mat labels, centers;
flags = GET_PARAM(2);
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
- cv::Size size = cv::Size(MWIDTH, MHEIGHT);
+ Size size = Size(MWIDTH, MHEIGHT);
src.create(size, type);
int row_idx = 0;
const int max_neighbour = MHEIGHT / K - 1;
/////////////////////////////// DistanceToCenters //////////////////////////////////////////
-CV_ENUM(DistType, NORM_L1, NORM_L2SQR);
+CV_ENUM(DistType, NORM_L1, NORM_L2SQR)
PARAM_TEST_CASE(distanceToCenters, DistType, bool)
{
- cv::Size size;
int distType;
bool useRoi;
- cv::Mat src, centers, src_roi, centers_roi;
- cv::ocl::oclMat ocl_src, ocl_centers, ocl_src_roi, ocl_centers_roi;
+
+ Mat src, centers, src_roi, centers_roi;
+ ocl::oclMat ocl_src, ocl_centers, ocl_src_roi, ocl_centers_roi;
virtual void SetUp()
{
void random_roi()
{
- Size roiSize_src = randomSize(10,1000);
- Size roiSize_centers = randomSize(10, 1000);
- roiSize_src.width = roiSize_centers.width;
+ Size roiSizeSrc = randomSize(1, MAX_VALUE);
+ Size roiSizeCenters = randomSize(1, MAX_VALUE);
+ roiSizeSrc.width = roiSizeCenters.width;
- Border srcBorder = randomBorder(0, useRoi ? 500 : 0);
- randomSubMat(src, src_roi, roiSize_src, srcBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
+ Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
+ randomSubMat(src, src_roi, roiSizeSrc, srcBorder, CV_32FC1, -MAX_VALUE, MAX_VALUE);
Border centersBorder = randomBorder(0, useRoi ? 500 : 0);
- randomSubMat(centers, centers_roi, roiSize_centers, centersBorder, CV_32FC1, -SHRT_MAX, SHRT_MAX);
-
- for(int i = 0; i<centers.rows; i++)
- centers.at<float>(i, randomInt(0,centers.cols-1)) = (float)randomDouble(SHRT_MAX, INT_MAX);
+ randomSubMat(centers, centers_roi, roiSizeCenters, centersBorder, CV_32FC1, -MAX_VALUE, MAX_VALUE);
- generateOclMat(ocl_src, ocl_src_roi, src, roiSize_src, srcBorder);
- generateOclMat(ocl_centers, ocl_centers_roi, centers, roiSize_centers, centersBorder);
+ for (int i = 0; i < centers.rows; i++)
+ centers.at<float>(i, randomInt(0, centers.cols)) = (float)randomDouble(SHRT_MAX, INT_MAX);
+ generateOclMat(ocl_src, ocl_src_roi, src, roiSizeSrc, srcBorder);
+ generateOclMat(ocl_centers, ocl_centers_roi, centers, roiSizeCenters, centersBorder);
}
-
};
OCL_TEST_P(distanceToCenters, Accuracy)
{
- for(int j = 0; j< LOOP_TIMES; j++)
+ for (int j = 0; j < LOOP_TIMES; j++)
{
random_roi();
- cv::ocl::oclMat ocl_dists;
- cv::ocl::oclMat ocl_labels;
-
- cv::ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src_roi, ocl_centers_roi, distType);
-
Mat labels, dists;
- ocl_labels.download(labels);
- ocl_dists.download(dists);
+ ocl::distanceToCenters(ocl_src_roi, ocl_centers_roi, dists, labels, distType);
- ASSERT_EQ(ocl_dists.cols, ocl_labels.rows);
+ EXPECT_EQ(dists.size(), labels.size());
Mat batch_dists;
-
cv::batchDistance(src_roi, centers_roi, batch_dists, CV_32FC1, noArray(), distType);
- std::vector<double> gold_dists_v;
+ std::vector<float> gold_dists_v;
+ gold_dists_v.reserve(batch_dists.rows);
- for(int i = 0; i<batch_dists.rows; i++)
+ for (int i = 0; i < batch_dists.rows; i++)
{
Mat r = batch_dists.row(i);
double mVal;
Point mLoc;
minMaxLoc(r, &mVal, NULL, &mLoc, NULL);
- int ocl_label = *(int*)labels.row(i).col(0).data;
- ASSERT_EQ(mLoc.x, ocl_label);
+ int ocl_label = labels.at<int>(i, 0);
+ EXPECT_EQ(mLoc.x, ocl_label);
- gold_dists_v.push_back(mVal);
+ gold_dists_v.push_back(static_cast<float>(mVal));
}
- Mat gold_dists(gold_dists_v);
- dists.convertTo(dists, CV_64FC1);
- double relative_error = cv::norm(gold_dists.t(), dists, NORM_INF|NORM_RELATIVE);
+
+ double relative_error = cv::norm(Mat(gold_dists_v), dists, NORM_INF | NORM_RELATIVE);
ASSERT_LE(relative_error, 1e-5);
}
}
-
-INSTANTIATE_TEST_CASE_P (OCL_ML, distanceToCenters, Combine(DistType::all(), Bool()) );
-
+INSTANTIATE_TEST_CASE_P (OCL_ML, distanceToCenters, Combine(DistType::all(), Bool()));
#endif
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